Overview

Dataset statistics

Number of variables32
Number of observations673
Missing cells987
Missing cells (%)4.6%
Duplicate rows3
Duplicate rows (%)0.4%
Total size in memory161.0 KiB
Average record size in memory245.0 B

Variable types

Categorical21
Numeric8
Text1
DateTime1
Boolean1

Alerts

is_single_plan has constant value "True"Constant
Dataset has 3 (0.4%) duplicate rowsDuplicates
Category is highly overall correlated with FieldHigh correlation
Delivery is highly overall correlated with PriceHigh correlation
Field is highly overall correlated with CategoryHigh correlation
Price is highly overall correlated with DeliveryHigh correlation
Arabic is highly imbalanced (73.1%)Imbalance
Bengali is highly imbalanced (86.2%)Imbalance
Chinese is highly imbalanced (87.1%)Imbalance
Dutch is highly imbalanced (81.5%)Imbalance
English is highly imbalanced (92.6%)Imbalance
Hebrew is highly imbalanced (92.6%)Imbalance
Hindi is highly imbalanced (53.2%)Imbalance
Indonesian is highly imbalanced (84.6%)Imbalance
Italian is highly imbalanced (74.4%)Imbalance
Portuguese is highly imbalanced (82.2%)Imbalance
Punjabi is highly imbalanced (85.4%)Imbalance
Russian is highly imbalanced (85.4%)Imbalance
Turkish is highly imbalanced (93.7%)Imbalance
Ukrainian is highly imbalanced (88.0%)Imbalance
Rating has 22 (3.3%) missing valuesMissing
Member Since has 312 (46.4%) missing valuesMissing
Avg Response Time has 333 (49.5%) missing valuesMissing
Last Delivery has 320 (47.5%) missing valuesMissing
Last Delivery has 79 (11.7%) zerosZeros
Order in Queue has 563 (83.7%) zerosZeros

Reproduction

Analysis started2024-05-27 15:23:36.683098
Analysis finished2024-05-27 15:23:50.664171
Duration13.98 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Category
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
Digital Marketing
158 
Data
111 
Lifestyle
100 
Business
65 
Music & Audio
58 
Other values (5)
181 

Length

Max length21
Median length18
Mean length12.555721
Min length4

Characters and Unicode

Total characters8450
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData
2nd rowData
3rd rowData
4th rowData
5th rowData

Common Values

ValueCountFrequency (%)
Digital Marketing 158
23.5%
Data 111
16.5%
Lifestyle 100
14.9%
Business 65
9.7%
Music & Audio 58
 
8.6%
Programming & Tech 55
 
8.2%
Graphics & Design 40
 
5.9%
Writing & Translation 35
 
5.2%
Video & Animation 30
 
4.5%
Photography 21
 
3.1%

Length

2024-05-27T18:53:50.815717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:51.007138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
218
17.2%
digital 158
12.5%
marketing 158
12.5%
data 111
8.8%
lifestyle 100
7.9%
business 65
 
5.1%
music 58
 
4.6%
audio 58
 
4.6%
tech 55
 
4.3%
programming 55
 
4.3%
Other values (7) 231
18.2%

Most occurring characters

ValueCountFrequency (%)
i 1085
12.8%
a 754
 
8.9%
t 648
 
7.7%
594
 
7.0%
e 548
 
6.5%
g 522
 
6.2%
n 483
 
5.7%
s 468
 
5.5%
r 399
 
4.7%
D 309
 
3.7%
Other values (21) 2640
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1085
12.8%
a 754
 
8.9%
t 648
 
7.7%
594
 
7.0%
e 548
 
6.5%
g 522
 
6.2%
n 483
 
5.7%
s 468
 
5.5%
r 399
 
4.7%
D 309
 
3.7%
Other values (21) 2640
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1085
12.8%
a 754
 
8.9%
t 648
 
7.7%
594
 
7.0%
e 548
 
6.5%
g 522
 
6.2%
n 483
 
5.7%
s 468
 
5.5%
r 399
 
4.7%
D 309
 
3.7%
Other values (21) 2640
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1085
12.8%
a 754
 
8.9%
t 648
 
7.7%
594
 
7.0%
e 548
 
6.5%
g 522
 
6.2%
n 483
 
5.7%
s 468
 
5.5%
r 399
 
4.7%
D 309
 
3.7%
Other values (21) 2640
31.2%

Field
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
seo-services
136 
online-tutoring
 
36
databases
 
36
data-engineering
 
34
game-development
 
28
Other values (30)
403 

Length

Max length29
Median length22
Mean length14.794948
Min length5

Characters and Unicode

Total characters9957
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowdata-engineering
2nd rowdata-engineering
3rd rowdata-engineering
4th rowdata-engineering
5th rowdata-engineering

Common Values

ValueCountFrequency (%)
seo-services 136
20.2%
online-tutoring 36
 
5.3%
databases 36
 
5.3%
data-engineering 34
 
5.1%
game-development 28
 
4.2%
producers 26
 
3.9%
financial-consulting-services 26
 
3.9%
personal-stylists 25
 
3.7%
data-science 21
 
3.1%
modeling-acting 21
 
3.1%
Other values (25) 284
42.2%

Length

2024-05-27T18:53:51.253378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seo-services 136
20.2%
databases 36
 
5.3%
online-tutoring 36
 
5.3%
data-engineering 34
 
5.1%
game-development 28
 
4.2%
producers 26
 
3.9%
financial-consulting-services 26
 
3.9%
personal-stylists 25
 
3.7%
data-science 21
 
3.1%
software-development 21
 
3.1%
Other values (25) 284
42.2%

Most occurring characters

ValueCountFrequency (%)
e 1356
13.6%
s 1121
11.3%
i 869
 
8.7%
n 766
 
7.7%
a 715
 
7.2%
- 658
 
6.6%
t 628
 
6.3%
o 592
 
5.9%
r 530
 
5.3%
g 392
 
3.9%
Other values (15) 2330
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1356
13.6%
s 1121
11.3%
i 869
 
8.7%
n 766
 
7.7%
a 715
 
7.2%
- 658
 
6.6%
t 628
 
6.3%
o 592
 
5.9%
r 530
 
5.3%
g 392
 
3.9%
Other values (15) 2330
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1356
13.6%
s 1121
11.3%
i 869
 
8.7%
n 766
 
7.7%
a 715
 
7.2%
- 658
 
6.6%
t 628
 
6.3%
o 592
 
5.9%
r 530
 
5.3%
g 392
 
3.9%
Other values (15) 2330
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1356
13.6%
s 1121
11.3%
i 869
 
8.7%
n 766
 
7.7%
a 715
 
7.2%
- 658
 
6.6%
t 628
 
6.3%
o 592
 
5.9%
r 530
 
5.3%
g 392
 
3.9%
Other values (15) 2330
23.4%

Seller Level
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1
221 
2
186 
3
143 
4
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Length

2024-05-27T18:53:51.419600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:51.544220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Most occurring characters

ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 221
32.8%
2 186
27.6%
3 143
21.2%
4 123
18.3%

Seller In Same Level
Real number (ℝ)

Distinct108
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7277.4859
Minimum7
Maximum75000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-27T18:53:51.754752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile33.2
Q1400
median1600
Q37900
95-th percentile41000
Maximum75000
Range74993
Interquartile range (IQR)7500

Descriptive statistics

Standard deviation12938.885
Coefficient of variation (CV)1.7779334
Kurtosis6.0323673
Mean7277.4859
Median Absolute Deviation (MAD)1445
Skewness2.5347043
Sum4897748
Variance1.6741475 × 108
MonotonicityNot monotonic
2024-05-27T18:53:51.935444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41000 46
 
6.8%
8100 45
 
6.7%
7900 42
 
6.2%
1000 21
 
3.1%
1100 16
 
2.4%
8900 14
 
2.1%
2400 14
 
2.1%
533 14
 
2.1%
2900 13
 
1.9%
23 13
 
1.9%
Other values (98) 435
64.6%
ValueCountFrequency (%)
7 1
 
0.1%
18 1
 
0.1%
22 7
1.0%
23 13
1.9%
25 3
 
0.4%
27 2
 
0.3%
32 7
1.0%
34 4
 
0.6%
35 2
 
0.3%
38 5
 
0.7%
ValueCountFrequency (%)
75000 3
 
0.4%
53000 6
 
0.9%
50472 2
 
0.3%
44000 6
 
0.9%
41000 46
6.8%
33000 2
 
0.3%
26000 3
 
0.4%
23000 1
 
0.1%
17000 2
 
0.3%
16000 1
 
0.1%

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.82122
Minimum3.24
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:52.108056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile5
Q110
median40
Q3100
95-th percentile483
Maximum20000
Range19996.76
Interquartile range (IQR)90

Descriptive statistics

Standard deviation1267.8905
Coefficient of variation (CV)5.9856634
Kurtosis189.88963
Mean211.82122
Median Absolute Deviation (MAD)30
Skewness13.172827
Sum142555.68
Variance1607546.3
MonotonicityNot monotonic
2024-05-27T18:53:52.493297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 79
 
11.7%
10 71
 
10.5%
50 51
 
7.6%
100 45
 
6.7%
20 38
 
5.6%
15 34
 
5.1%
30 31
 
4.6%
25 26
 
3.9%
40 19
 
2.8%
200 19
 
2.8%
Other values (95) 260
38.6%
ValueCountFrequency (%)
3.24 1
 
0.1%
4.48 8
 
1.2%
5 79
11.7%
6.49 1
 
0.1%
8.95 9
 
1.3%
10 71
10.5%
13.44 3
 
0.4%
15 34
5.1%
17.91 4
 
0.6%
17.92 1
 
0.1%
ValueCountFrequency (%)
20000 2
0.3%
11400 1
0.1%
10000 1
0.1%
4500 1
0.1%
3000 1
0.1%
2774.15 1
0.1%
2500 1
0.1%
2000 2
0.3%
1800 1
0.1%
1600 1
0.1%

Delivery
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9895988
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-27T18:53:52.670156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile29.4
Maximum90
Range89
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.1075687
Coefficient of variation (CV)1.5205641
Kurtosis34.753816
Mean5.9895988
Median Absolute Deviation (MAD)2
Skewness4.9116201
Sum4031
Variance82.947808
MonotonicityNot monotonic
2024-05-27T18:53:53.009324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 142
21.1%
1 130
19.3%
3 114
16.9%
7 81
12.0%
5 56
 
8.3%
4 39
 
5.8%
10 29
 
4.3%
30 28
 
4.2%
14 27
 
4.0%
21 13
 
1.9%
Other values (5) 14
 
2.1%
ValueCountFrequency (%)
1 130
19.3%
2 142
21.1%
3 114
16.9%
4 39
 
5.8%
5 56
 
8.3%
6 7
 
1.0%
7 81
12.0%
10 29
 
4.3%
14 27
 
4.0%
21 13
 
1.9%
ValueCountFrequency (%)
90 3
 
0.4%
60 1
 
0.1%
45 2
 
0.3%
30 28
 
4.2%
29 1
 
0.1%
21 13
 
1.9%
14 27
 
4.0%
10 29
 
4.3%
7 81
12.0%
6 7
 
1.0%

Rating
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)1.8%
Missing22
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean4.9175115
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:53.134544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.8
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.35884509
Coefficient of variation (CV)0.072972903
Kurtosis96.776024
Mean4.9175115
Median Absolute Deviation (MAD)0
Skewness-9.4884561
Sum3201.3
Variance0.1287698
MonotonicityNot monotonic
2024-05-27T18:53:53.284128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 438
65.1%
4.9 149
 
22.1%
4.8 37
 
5.5%
4.7 11
 
1.6%
1 4
 
0.6%
4 3
 
0.4%
4.6 3
 
0.4%
4.5 2
 
0.3%
4.1 1
 
0.1%
1.7 1
 
0.1%
Other values (2) 2
 
0.3%
(Missing) 22
 
3.3%
ValueCountFrequency (%)
1 4
 
0.6%
1.7 1
 
0.1%
3 1
 
0.1%
4 3
 
0.4%
4.1 1
 
0.1%
4.3 1
 
0.1%
4.5 2
 
0.3%
4.6 3
 
0.4%
4.7 11
 
1.6%
4.8 37
5.5%
ValueCountFrequency (%)
5 438
65.1%
4.9 149
 
22.1%
4.8 37
 
5.5%
4.7 11
 
1.6%
4.6 3
 
0.4%
4.5 2
 
0.3%
4.3 1
 
0.1%
4.1 1
 
0.1%
4 3
 
0.4%
3 1
 
0.1%

Rating Count
Real number (ℝ)

Distinct222
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.67608
Minimum1
Maximum8847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:53.507606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q18
median25
Q386
95-th percentile860.4
Maximum8847
Range8846
Interquartile range (IQR)78

Descriptive statistics

Standard deviation681.48726
Coefficient of variation (CV)3.5186961
Kurtosis86.961379
Mean193.67608
Median Absolute Deviation (MAD)21
Skewness8.2555194
Sum130344
Variance464424.89
MonotonicityNot monotonic
2024-05-27T18:53:53.688979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34
 
5.1%
3 31
 
4.6%
5 25
 
3.7%
2 22
 
3.3%
4 19
 
2.8%
12 17
 
2.5%
6 17
 
2.5%
18 15
 
2.2%
11 14
 
2.1%
10 13
 
1.9%
Other values (212) 466
69.2%
ValueCountFrequency (%)
1 34
5.1%
2 22
3.3%
3 31
4.6%
4 19
2.8%
5 25
3.7%
6 17
2.5%
7 12
 
1.8%
8 11
 
1.6%
9 5
 
0.7%
10 13
 
1.9%
ValueCountFrequency (%)
8847 1
0.1%
8792 1
0.1%
5291 2
0.3%
3577 1
0.1%
3154 1
0.1%
2943 1
0.1%
2852 1
0.1%
2678 1
0.1%
2528 1
0.1%
2335 1
0.1%
Distinct71
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:53.901552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.4977712
Min length4

Characters and Unicode

Total characters5719
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)3.4%

Sample

1st rowPakistan
2nd rowPakistan
3rd rowSingapore
4th rowPakistan
5th rowSingapore
ValueCountFrequency (%)
pakistan 200
24.7%
united 115
14.2%
states 79
 
9.7%
india 56
 
6.9%
kingdom 34
 
4.2%
bangladesh 26
 
3.2%
argentina 16
 
2.0%
sri 15
 
1.8%
lanka 15
 
1.8%
canada 14
 
1.7%
Other values (68) 241
29.7%
2024-05-27T18:53:54.347375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 903
15.8%
n 616
10.8%
i 599
10.5%
t 531
 
9.3%
e 417
 
7.3%
s 347
 
6.1%
d 282
 
4.9%
k 233
 
4.1%
P 212
 
3.7%
r 149
 
2.6%
Other values (41) 1430
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 903
15.8%
n 616
10.8%
i 599
10.5%
t 531
 
9.3%
e 417
 
7.3%
s 347
 
6.1%
d 282
 
4.9%
k 233
 
4.1%
P 212
 
3.7%
r 149
 
2.6%
Other values (41) 1430
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 903
15.8%
n 616
10.8%
i 599
10.5%
t 531
 
9.3%
e 417
 
7.3%
s 347
 
6.1%
d 282
 
4.9%
k 233
 
4.1%
P 212
 
3.7%
r 149
 
2.6%
Other values (41) 1430
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 903
15.8%
n 616
10.8%
i 599
10.5%
t 531
 
9.3%
e 417
 
7.3%
s 347
 
6.1%
d 282
 
4.9%
k 233
 
4.1%
P 212
 
3.7%
r 149
 
2.6%
Other values (41) 1430
25.0%

Member Since
Date

MISSING 

Distinct111
Distinct (%)30.7%
Missing312
Missing (%)46.4%
Memory size10.5 KiB
Minimum2012-06-01 00:00:00
Maximum2024-05-01 00:00:00
2024-05-27T18:53:54.552450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:54.738071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Avg Response Time
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)6.5%
Missing333
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean5.7617647
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:54.955221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile12.05
Maximum288
Range287
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.403206
Coefficient of variation (CV)4.0618121
Kurtosis106.66097
Mean5.7617647
Median Absolute Deviation (MAD)0
Skewness9.7174485
Sum1959
Variance547.71004
MonotonicityNot monotonic
2024-05-27T18:53:55.111230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 196
29.1%
2 35
 
5.2%
3 29
 
4.3%
4 22
 
3.3%
5 13
 
1.9%
7 7
 
1.0%
8 6
 
0.9%
72 5
 
0.7%
6 5
 
0.7%
9 4
 
0.6%
Other values (12) 18
 
2.7%
(Missing) 333
49.5%
ValueCountFrequency (%)
1 196
29.1%
2 35
 
5.2%
3 29
 
4.3%
4 22
 
3.3%
5 13
 
1.9%
6 5
 
0.7%
7 7
 
1.0%
8 6
 
0.9%
9 4
 
0.6%
10 2
 
0.3%
ValueCountFrequency (%)
288 1
 
0.1%
264 1
 
0.1%
96 1
 
0.1%
72 5
0.7%
48 1
 
0.1%
24 3
0.4%
23 1
 
0.1%
21 2
 
0.3%
19 1
 
0.1%
13 1
 
0.1%

Last Delivery
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)6.5%
Missing320
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean31.878187
Minimum0
Maximum730
Zeros79
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2024-05-27T18:53:55.328751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q321
95-th percentile210
Maximum730
Range730
Interquartile range (IQR)20

Descriptive statistics

Standard deviation85.851281
Coefficient of variation (CV)2.6931043
Kurtosis27.721558
Mean31.878187
Median Absolute Deviation (MAD)4
Skewness4.6765103
Sum11253
Variance7370.4425
MonotonicityNot monotonic
2024-05-27T18:53:55.463006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 79
 
11.7%
1 41
 
6.1%
7 39
 
5.8%
2 32
 
4.8%
30 25
 
3.7%
3 20
 
3.0%
21 20
 
3.0%
5 16
 
2.4%
4 15
 
2.2%
14 14
 
2.1%
Other values (13) 52
 
7.7%
(Missing) 320
47.5%
ValueCountFrequency (%)
0 79
11.7%
1 41
6.1%
2 32
4.8%
3 20
 
3.0%
4 15
 
2.2%
5 16
 
2.4%
6 5
 
0.7%
7 39
5.8%
14 14
 
2.1%
21 20
 
3.0%
ValueCountFrequency (%)
730 2
 
0.3%
365 5
0.7%
330 3
 
0.4%
270 1
 
0.1%
240 1
 
0.1%
210 10
1.5%
180 3
 
0.4%
150 3
 
0.4%
120 4
 
0.6%
90 4
 
0.6%

Order in Queue
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38038633
Minimum0
Maximum39
Zeros563
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-27T18:53:55.586843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8112665
Coefficient of variation (CV)4.7616497
Kurtosis313.40799
Mean0.38038633
Median Absolute Deviation (MAD)0
Skewness15.554542
Sum256
Variance3.2806862
MonotonicityNot monotonic
2024-05-27T18:53:55.765345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 563
83.7%
1 62
 
9.2%
2 22
 
3.3%
3 12
 
1.8%
4 5
 
0.7%
6 3
 
0.4%
5 3
 
0.4%
8 1
 
0.1%
39 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
0 563
83.7%
1 62
 
9.2%
2 22
 
3.3%
3 12
 
1.8%
4 5
 
0.7%
5 3
 
0.4%
6 3
 
0.4%
8 1
 
0.1%
14 1
 
0.1%
39 1
 
0.1%
ValueCountFrequency (%)
39 1
 
0.1%
14 1
 
0.1%
8 1
 
0.1%
6 3
 
0.4%
5 3
 
0.4%
4 5
 
0.7%
3 12
 
1.8%
2 22
 
3.3%
1 62
 
9.2%
0 563
83.7%

is_single_plan
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
True
673 
ValueCountFrequency (%)
True 673
100.0%
2024-05-27T18:53:55.879430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Arabic
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
642 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Length

2024-05-27T18:53:56.034076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:56.169444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 642
95.4%
1 31
 
4.6%

Bengali
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
660 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Length

2024-05-27T18:53:56.288914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:56.408451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 660
98.1%
1 13
 
1.9%

Chinese
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
661 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Length

2024-05-27T18:53:56.595717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:56.737061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 661
98.2%
1 12
 
1.8%

Dutch
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
654 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

Length

2024-05-27T18:53:56.874627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:57.069311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 654
97.2%
1 19
 
2.8%

English
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1
667 
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

Length

2024-05-27T18:53:57.190497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:57.307453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 667
99.1%
0 6
 
0.9%

French
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
586 
1
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

Length

2024-05-27T18:53:57.420929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:57.558065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586
87.1%
1 87
 
12.9%

German
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
585 
1
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Length

2024-05-27T18:53:57.726066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:57.832846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 585
86.9%
1 88
 
13.1%

Hebrew
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
667 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Length

2024-05-27T18:53:57.971660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:58.132220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 667
99.1%
1 6
 
0.9%

Hindi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
606 
1
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Length

2024-05-27T18:53:58.313562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:58.445482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 606
90.0%
1 67
 
10.0%

Indonesian
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
658 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Length

2024-05-27T18:53:58.560018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:58.698925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 658
97.8%
1 15
 
2.2%

Italian
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
644 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Length

2024-05-27T18:53:58.844057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:58.949031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 644
95.7%
1 29
 
4.3%

Portuguese
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
655 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Length

2024-05-27T18:53:59.099833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:59.226529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 655
97.3%
1 18
 
2.7%

Punjabi
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
659 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Length

2024-05-27T18:53:59.387833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:59.539784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Russian
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
659 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Length

2024-05-27T18:53:59.655475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:53:59.978691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 659
97.9%
1 14
 
2.1%

Spanish
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
543 
1
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Length

2024-05-27T18:54:00.136490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:54:00.295079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Most occurring characters

ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 543
80.7%
1 130
 
19.3%

Turkish
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
668 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Length

2024-05-27T18:54:00.456568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:54:00.637032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 668
99.3%
1 5
 
0.7%

Ukrainian
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
662 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Length

2024-05-27T18:54:00.794163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:54:00.913428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 662
98.4%
1 11
 
1.6%

Urdu
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
568 
1
105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters673
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Length

2024-05-27T18:54:01.129263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T18:54:01.285115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 568
84.4%
1 105
 
15.6%

Interactions

2024-05-27T18:53:48.456870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:39.371648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:40.885211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.207544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:43.755980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.028006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.184936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.285152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.663123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:39.563376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.043478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.386831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:43.947861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.218361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.346937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.399693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.800217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:39.764865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.222975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.544913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.110113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.384575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.458805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.649525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.921229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:39.997936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.365757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.665554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.230545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.510328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.570718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.758417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:49.056026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:40.196336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.546520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.838871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.372222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.628362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.673686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.885322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:49.227455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:40.341351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.707237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:43.115128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.499986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.768574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.816349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.011482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:49.350291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:40.543648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:41.874897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:43.325641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.667428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:45.912372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.966151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.129506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:49.486359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:40.719602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:42.003024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:43.550256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:44.849483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:46.059244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:47.113091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T18:53:48.287318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-27T18:54:01.463240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ArabicAvg Response TimeBengaliCategoryChineseDeliveryDutchEnglishFieldFrenchGermanHebrewHindiIndonesianItalianLast DeliveryOrder in QueuePortuguesePricePunjabiRatingRating CountRussianSeller In Same LevelSeller LevelSpanishTurkishUkrainianUrdu
Arabic1.000-0.1030.0000.0340.000-0.0970.0000.0840.2080.2400.0000.0000.0000.0000.000-0.013-0.0230.000-0.0840.000-0.070-0.0200.0000.0560.1060.0000.0000.0000.000
Avg Response Time-0.1031.0000.0000.0580.0440.1550.0000.0000.0000.0000.0000.2250.0000.0000.0000.1220.0590.0000.1080.000-0.0270.1270.000-0.0330.0560.1450.0000.0000.000
Bengali0.0000.0001.0000.0540.0000.0030.0000.0000.0000.0000.0000.0000.2940.0000.000-0.0450.0050.000-0.0260.000-0.0400.0410.0000.0360.0110.0000.0000.0000.024
Category0.0340.0580.0541.0000.000-0.0270.0000.0000.9810.0660.3120.0500.1830.0820.0580.0810.2100.190-0.0910.143-0.0060.1210.0000.0440.1100.1120.0730.0880.196
Chinese0.0000.0440.0000.0001.000-0.0880.0000.0000.3070.0000.0000.0000.0000.0000.000-0.0620.0290.000-0.0600.000-0.006-0.0290.0000.0070.0000.0000.0000.0000.000
Delivery-0.0970.1550.003-0.027-0.0881.0000.0000.0000.1590.0240.0310.0000.0000.0000.0000.107-0.0330.2180.5010.0000.082-0.0230.000-0.0830.0390.0690.0000.0000.042
Dutch0.0000.0000.0000.0000.0000.0001.0000.0000.0330.0720.1280.0000.0160.0000.000-0.0690.0260.0000.0560.0000.0130.0330.000-0.0930.0850.0160.0000.0000.000
English0.0840.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000-0.0300.0420.000-0.0060.000-0.036-0.0050.000-0.0760.0280.0000.0000.0000.000
Field0.2080.0000.0000.9810.3070.1590.0330.0001.0000.1400.2880.1810.1660.0590.0000.0730.0570.266-0.0910.199-0.0590.2330.0940.4040.1680.1730.0000.1270.211
French0.2400.0000.0000.0660.0000.0240.0720.0000.1401.0000.1820.0000.1140.0190.072-0.000-0.0380.032-0.0100.013-0.0390.0740.0000.0560.0000.2400.0000.0000.031
German0.0000.0000.0000.3120.0000.0310.1280.0000.2880.1821.0000.0000.0840.0000.000-0.081-0.0460.0000.0190.000-0.0100.1070.0000.0760.0960.1920.0000.0000.007
Hebrew0.0000.2250.0000.0500.0000.0000.0000.0000.1810.0000.0001.0000.0000.0000.000-0.021-0.0020.0000.0680.000-0.0350.0620.0150.0040.0530.0000.0000.0000.000
Hindi0.0000.0000.2940.1830.0000.0000.0160.0000.1660.1140.0840.0001.0000.0000.0440.0130.0010.010-0.0510.173-0.0600.0210.0000.0130.0980.1260.0000.0000.160
Indonesian0.0000.0000.0000.0820.0000.0000.0000.0000.0590.0190.0000.0000.0001.0000.000-0.1030.0120.000-0.0830.0000.0470.0530.000-0.0030.0420.0470.0240.0000.033
Italian0.0000.0000.0000.0580.0000.0000.0000.0000.0000.0720.0000.0000.0440.0001.0000.092-0.0530.000-0.0070.000-0.040-0.0420.000-0.0270.0030.1600.0000.0000.047
Last Delivery-0.0130.122-0.0450.081-0.0620.107-0.069-0.0300.073-0.000-0.081-0.0210.013-0.1030.0921.000-0.4380.0000.1580.0000.068-0.3250.0000.2240.2340.0000.0000.0000.000
Order in Queue-0.0230.0590.0050.2100.029-0.0330.0260.0420.057-0.038-0.046-0.0020.0010.012-0.053-0.4381.0000.000-0.0560.000-0.0280.2450.000-0.0650.0600.0000.0000.0000.000
Portuguese0.0000.0000.0000.1900.0000.2180.0000.0000.2660.0320.0000.0000.0100.0000.0000.0000.0001.000-0.0130.0000.0320.0310.0000.0050.0180.1350.0070.0000.044
Price-0.0840.108-0.026-0.091-0.0600.5010.056-0.006-0.091-0.0100.0190.068-0.051-0.083-0.0070.158-0.056-0.0131.0000.0000.161-0.1730.000-0.3090.0410.0360.0000.0000.102
Punjabi0.0000.0000.0000.1430.0000.0000.0000.0000.1990.0130.0000.0000.1730.0000.0000.0000.0000.0000.0001.0000.0090.0280.0000.0740.0000.0000.0000.0000.236
Rating-0.070-0.027-0.040-0.006-0.0060.0820.013-0.036-0.059-0.039-0.010-0.035-0.0600.047-0.0400.068-0.0280.0320.1610.0091.000-0.0500.000-0.1580.0450.0420.0000.0000.000
Rating Count-0.0200.1270.0410.121-0.029-0.0230.033-0.0050.2330.0740.1070.0620.0210.053-0.042-0.3250.2450.031-0.1730.028-0.0501.0000.0000.1110.1300.1130.0000.0000.085
Russian0.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000-0.0360.0000.0000.0000.2660.029
Seller In Same Level0.056-0.0330.0360.0440.007-0.083-0.093-0.0760.4040.0560.0760.0040.013-0.003-0.0270.224-0.0650.005-0.3090.074-0.1580.111-0.0361.0000.3050.0000.0000.0000.000
Seller Level0.1060.0560.0110.1100.0000.0390.0850.0280.1680.0000.0960.0530.0980.0420.0030.2340.0600.0180.0410.0000.0450.1300.0000.3051.0000.0500.0790.0210.049
Spanish0.0000.1450.0000.1120.0000.0690.0160.0000.1730.2400.1920.0000.1260.0470.1600.0000.0000.1350.0360.0000.0420.1130.0000.0000.0501.0000.0000.0000.094
Turkish0.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0790.0001.0000.0000.000
Ukrainian0.0000.0000.0000.0880.0000.0000.0000.0000.1270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2660.0000.0210.0000.0001.0000.007
Urdu0.0000.0000.0240.1960.0000.0420.0000.0000.2110.0310.0070.0000.1600.0330.0470.0000.0000.0440.1020.2360.0000.0850.0290.0000.0490.0940.0000.0071.000

Missing values

2024-05-27T18:53:49.775248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-27T18:53:50.261738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-27T18:53:50.504856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CategoryFieldSeller LevelSeller In Same LevelPriceDeliveryRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu
9Datadata-engineering2293100.055.05.0PakistanNaNNaNNaN0True000010000000000001
10Datadata-engineering229310.034.713.0PakistanNaNNaNNaN0True000010000000000000
11Datadata-engineering2293100.0105.05.0SingaporeNaNNaNNaN0True000010000000000000
21Datadata-engineering229310.024.94.0PakistanNaNNaNNaN0True000010000000000000
22Datadata-engineering229350.035.03.0SingaporeNaNNaNNaN0True000010000000000000
28Datadata-engineering229340.014.93.0PakistanNaNNaNNaN0True000010000000000000
32Datadata-engineering229320.025.012.0CanadaNaNNaNNaN0True000010000000000000
35Datadata-engineering229380.025.09.0IndiaNaNNaNNaN0True000010000000000000
38Datadata-engineering229350.025.03.0MoroccoNaNNaNNaN0True100011000000001000
40Datadata-engineering22935.015.037.0IndonesiaNaNNaNNaN0True000010000100000000
CategoryFieldSeller LevelSeller In Same LevelPriceDeliveryRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu
6137Businesssoftware-management250225.0045.026.0United StatesNaN4.03.02True000010000000000000
6146Businesssoftware-management250295.0035.025.0Pakistan2022-05-017.07.00True000010100001001000
6151Businesssoftware-management250220.0025.018.0Pakistan2018-12-011.07.00True000010000000000001
6168Businesssoftware-management3329600.0074.611.0Pakistan2016-10-011.00.00True000010000000000000
6192Businesssoftware-management332990.0025.03.0Pakistan2016-08-012.02.00True000010000000000000
6202Businesssoftware-management3329179.10145.034.0India2020-11-013.030.00True000010000000000000
6214Businesssoftware-management455350.0075.033.0Pakistan2019-05-011.02.00True000010100000001001
6238Businesssoftware-management455223.88105.04.0Pakistan2018-09-011.06.00True000010000000000001
6255Businesssoftware-management455250.00105.04.0Pakistan2018-09-011.06.00True000010000000000001
6260Businesssoftware-management45550.00215.036.0Bangladesh2015-03-011.07.00True000010000000000000

Duplicate rows

Most frequently occurring

CategoryFieldSeller LevelSeller In Same LevelPriceDeliveryRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu# duplicates
0Datadata-mining46530.025.071.0United States2020-12-017.00.01True0000100000000010002
1Graphics & Designfashion-design41725.035.0417.0Indonesia2016-07-011.00.03True0000100000000000002
2Graphics & Designsocial-media-design425850.025.05291.0Romania2013-10-014.00.04True0000100000000000002